Josef Kainz

Local Model Networks with Modified Parabolic Membership Functions

Models in today’s microcontrollers, e.g. engine control units, are realized with a multitude of characteristic curves and look-up tables. The increasing complexity of these models causes an exponential growth of the required calibration memory. Hence, neural networks, e.g. local model networks, which provide a solution for this problem, become more important for modeling. Usually Gaussians are used as membership functions. The calculation of the therefore necessary exponential function is very demanding on low performance microcontrollers. Thus in this paper a modified membership function for the efficient implementation of local model networks is proposed. Their advantages compared to standard local model networks and to look-up tables are illustrated by the application of an intake manifold model of a combustion engine.

Local model networks

A multitude of functions in modern electronic control units (ECU) uses characterstic curves and look-up tables (LUT) for modeling. The increasing complexity of these models causes an exponential growing of the required calibration memory. Hence, this way of modeling with LUT is not feasible any longer. On this account, the University of Siegen developed in cooperation with Continental Automotive GmbH local model networks in fixed-point arithmetic for series-ECU, which provide a solution for this problem.